Occurrence rates of SuperDARN ground scatter echoes and electron density in the ionosphere
Bibliographic record
Abstract
Ground scatter (GS) echoes in Super Dual Auroral Radar Network (SuperDARN) observations have been always expected to occur under high-enough electron density in the ionosphere providing sufficient bending of HF radio wave paths toward the ground. In this study we provide direct evidence statistically supporting this notion by comparing the GS occurrence rate for the Rankin Inlet SuperDARN radar and the F region peak electron density N m F 2 measured at Resolute Bay by the CADI ionosonde and incoherent scatter radars RISR-N/C. We show that the occurrence rate increases with N m F 2 roughly linearly up to about ~4·10 11 m -3 , and the trend saturates at larger N m F 2 . One expected consequence of this relationship is correlation in seasonal and solar cycle variations of the GS echo occurrence rate and N m F 2 . GS occurrence rates for a number of SuperDARN radars at middle latitudes, in the auroral zone and in the polar cap are considered separately for daytime and nighttime. The data indicate that the daytime occurrence rates are maximized in winter and nighttime occurrence rates are maximized in summer for middle latitude and auroral zone radars in the Northern Hemisphere, consistent with the Winter Anomaly (WA) phenomenon. The effect is most evident in the North American and Japanize sectors, and the quality of WA signatures deteriorates in the European and, especially, in the Australian sectors. The effect does not exist in the South American sector and in the polar caps of both hemispheres.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".